1. Field of the Invention
The present invention relates to a grouping technique, and more particularly to a method and apparatus for clustering digital images of objects, such as people.
2. Description of the Related Art
Identification and classification of objects in images is an important application useful in many fields. For example, identification and classification of people in images is important and useful for automatic organization and retrieval of images in photo albums, for security applications, etc. Face recognition has been used to identify people in photographs and in digital image data. Reliable face recognition and classification, however, is difficult to achieve because of variations in image conditions and human imaging, including lighting variations, pose changes, etc.
A technique for classification of images based on the identities of the people in images has been studied in “Automated Annotation of Human Faces in Family Albums”, by L. Zhang, L. Chen, M. Li, and H. Zhang, in Proc. ACM Multimedia, MM'03, Berkeley, Calif., USA, Nov. 2-8, 2003. In this publication, facial features and contextual features are used to characterize people in images. However, in this publication, grouping of images based on identities of people cannot be done automatically, and only an image search is available.
K-means clustering algorithms and spectral clustering algorithms have been used to group objects into classes. Spectral clustering algorithms are described in “Normalized cuts and image segmentation”, by J. Shi and J. Malik, in Proc. CVPR, pages 731-737, June 1997, “Segmentation using eigenvectors: a Unifying View”, by Y. Weiss, in Proc. ICCV, 1999, and “On spectral clustering: Analysis and an algorithm”, by A. Y. Ng, M. I. Jordan, and Y. Weiss, in NIPS 14, 2002. However, K-means clustering easily fails when object clusters do not correspond to convex regions, which is the case for human clustering when imaging conditions change due to variations, such as lighting changes and pose changes. While spectral clustering methods do not have this limitation, it is challenging to enforce context information such as hard constraints, in spectral clustering algorithms. Hard constraints, which provide information on whether two objects belong or not to the same cluster, provide important and useful cues for clustering of objects.
Disclosed embodiments of this application address issues associated with human recognition and classification, by using a constrained spectral clustering method and apparatus that can enforce hard constraints. The method and apparatus use a new clothes recognition algorithm and perform a principled integration of face and clothes recognition data. The constrained spectral clustering method and apparatus can enforce hard constraints such as logic-based constraints from context cues and user feedback.